2973 Design courses in Chigwell delivered Live Online

Duration 5 Days 30 CPD hours This course is intended for Entry- to mid-level network engineers Network administrators Network support technicians Help desk technicians Overview After taking this training, you should be able to: Illustrate the hierarchical network design model and architecture using the access, distribution, and core layers Compare and contrast the various hardware and software switching mechanisms and operation while defining the Ternary Content Addressable Memory (TCAM) and Content Addressable Memory (CAM) along with process switching, fast switching, and Cisco Express Forwarding concepts Troubleshoot Layer 2 connectivity using VLANs and trunking Implement redundant switched networks using Spanning Tree Protocol Troubleshoot link aggregation using Etherchannel Describe the features, metrics, and path selection concepts of Enhanced Interior Gateway Routing Protocol (EIGRP) Implement and optimize Open Shortest Path First (OSPF)v2 and OSPFv3, including adjacencies, packet types and areas, summarization, and route filtering for IPv4 and IPv6 Implement External Border Gateway Protocol (EBGP) interdomain routing, path selection, and single and dual-homed networking Implement network redundancy using protocols such as Hot Standby Routing Protocol (HSRP) and Virtual Router Redundancy Protocol (VRRP) Implement internet connectivity within Enterprise using static and dynamic Network Address Translation (NAT) Describe the virtualization technology of servers, switches, and the various network devices and components Implement overlay technologies such as Virtual Routing and Forwarding (VRF), Generic Routing Encapsulation (GRE), VPN, and Location Identifier Separation Protocol (LISP) Describe the components and concepts of wireless networking, including Radio Frequency (RF) and antenna characteristics, and define the specific wireless standards Describe the various wireless deployment models available, including autonomous Access Point (AP) deployments and cloud-based designs within the centralized Cisco Wireless LAN Controller (WLC) architecture Describe wireless roaming and location services The Implementing and Operating Cisco Enterprise Network Core Technologies (ENCOR) v1.3 training gives you the knowledge and skills needed to install, configure, operate, and troubleshoot an enterprise network and introduces you to overlay network design by using SD-Access and SD-WAN solutions. You?ll also learn to understand and implement security principles and automation and programmability within an enterprise network. Course Outline Examining Cisco Enterprise Network Architecture Exploring Cisco Switching Paths Implementing Campus LAN Connectivity Building Redundant Switched Topology Implementing Layer 2 Port Aggregation Understanding EIGRP Implementing OSPF Optimizing OSPF Exploring EBGP Implementing Network Redundancy Implementing NAT Introducing Virtualization Protocols and Techniques Understanding Virtual Private Networks and Interfaces Understanding Wireless Principles Examining Wireless Deployment Options Understanding Wireless Roaming and Location Services Examining Wireless AP Operation Implementing Wireless Client Authentication Troubleshooting Wireless Client Connectivity Implementing Network Services Using Network Analysis Tools Implementing Infrastructure Security Implementing Secure Access Control Discovering the Basics of Python Programming Discovering Network Programmability Protocols Implementing Layer 2 Port Aggregation Discovering Multicast Protocols Understanding QoS Exploring Enterprise Network Security Architecture Exploring Automation and Assurance Using Cisco DNA Center Examining the Cisco SD-Access Solution Understanding the Working Principles of the Cisco SD-WAN Solution

Duration 5 Days 30 CPD hours This course is intended for This course is intended for: Solutions Architects who are new to designing and building cloud architectures Data Center Architects who are migrating from on-premises environment to cloud architectures Other IT/cloud roles who want to understand how to design and build cloud architectures Overview In this course, you will learn how to: Make architectural decisions based on AWS architectural principles and best practices Use AWS services to make your infrastructure scalable, reliable, and highly available Use AWS Managed Services to enable greater flexibility and resiliency in an infrastructure Make an AWS-based infrastructure more efficient to increase performance and reduce costs Use the Well Architected Framework to improve architectures with AWS solutions This course covers all aspects of how to architect for the cloud over four-and-a-half-days. It covers topics from Architecting on AWS and Advanced Architecting on AWS to offer an immersive course in cloud architecture. You will learn how to design cloud architectures, starting small and working to large-scale enterprise level designs-and everything in between. Starting with the Well-Architected Framework, you will learn important architecting information for AWS services including: compute, storage, database, networking, security, monitoring, automation, optimization, benefits of de-coupling applications and serverless, building for resilience, and understanding costs Module 1: Introduction The real story of AWS Well-Architected Framework Six advantages of the cloud Global infrastructure Module 2: The Simplest Architectures S3 Glacier Choosing your regions Hands-on lab: Static Website Module 3: Adding a Compute Layer EC2 Storage solutions for instances Purchasing options such as dedicated host vs instances Module 4: Adding a Database Layer Relational vs non-relational Managed databases RDS Dynamo DB Neptune Hands-on lab: Deploying a web application on AWS Module 5: Networking in AWS Part 1 VPC CIDR and subnets Public vs private subnets NAT and internet gateway Security groups Module 6: Networking in AWS Part 2 Virtual Private Gateway VPN Direct Connect VPC peering Transit Gateway VPC Endpoints Elastic Load Balancer Route 53 Hands-on lab: Creating a VPC Module 7: AWS Identity and Access Management (IAM) IAM Identity federation Cognito Module 8: Organizations Organizations Multiple account management Tagging strategies Module 9: Elasticity, High Availability, and Monitoring Elasticity vs inelasticity Monitoring with CloudWatch, CloudTrail, and VPC Flow Logs Auto scaling Scaling databases Hands-on lab: Creating a highly available environment Module 10: Automation Why automate? CloudFormation AWS Quick Starts AWS Systems Manager AWS OpsWorks AWS Elastic Beanstalk Module 11: Deployment Methods Why use a deployment method? Blue green and canary deployment Tools to implement your deployment methods CI/CD Hands-on lab: Automating infrastructure deployment Module 12: Caching When and why you should cache your data Cloudfront Elasticache (Redis/Memcached) DynamoDB Accelerator Module 13: Security of Your Data Shared responsibility model Data classification Encryption Automatic data security Module 14: Building Decoupled Architecture Tight coupling vs loose coupling SQS SNS Module 15: Optimizations and Review Review questions Best practices Activity: Design and architecture - two trues and one lie Module 16: Microservices What is a microservice? Containers ECS Fargate EKS Module 17: Serverless Why use serverless? Lambda API Gateway AWS Step Functions Hands-on lab: Implementing a serverless architecture with AWS Managed Services Module 18: Building for Resilience Using managed services greatly increases resiliency Serverless for resiliency Issues with microservices to be aware of DDoS Hands-on lab: Amazon CloudFront content delivery and automating WAF rules Module 19: Networking in AWS Part 3 Elastic Network Adapter Maximum transmission units Global Accelerator Site to site VPN Transit Gateway Module 20: Understanding Costs Simple monthly calculator Right sizing your instances Price sensitive architecture examples Module 21: Migration Strategies Cloud migration strategies Planning Migrating Optimizing Hands-on lab: Application deployment using AWS Fargate Module 22: RTO/RPO and Backup Recovery Setup Disaster planning Recovery options Module 23: Final Review Architecting advice Service use case questions Example test questions Additional course details: Nexus Humans Architecting on AWS - Accelerator training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Architecting on AWS - Accelerator course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.

Duration 2 Days 12 CPD hours This course is intended for Participants who have actual experience in the data centre and/or IT infrastructures are best suited. Attendance of the CDCP© course is recommended but not a requirement. Overview After completion of the course the participant will be able to: 1. Develop and review their data centre strategy 2. Use different risk assessment methodologies together with practical tips specifically for data centre migrations to reduce the risk during a data centre migration 3. Understand different migration strategies 4. Understand the legal aspects when migrating a data centre 5. Understand the importance of Business Service Reviews and Service Level Objectives 6. Size and design the target data centre 7. Understand the importance of detailed discovery and how dependencies influence migration waves 8. Understand the safety requirements during migration 19. Get lots of practical tips when moving to another data centre This course is designed to expose participants to a step-by-step methodology which will enable them to reduce the risks involved when undertaking a data centre migration. It will also give participants a lot of valuable practical hints and tips by trainers having extensive experience in moving and consolidating mission critical data centre. Data Centre Strategy Data centre lifecycle Reasons to migrate a data centre Alternatives to data centre migration Consolidation Outsourcing Cloud computing Upgrade existing data centre or build new Project Management Project management and methods Scope statement Statement Of Work (SOW) Work Breakdown Structure (WBS) Allocate time to the project Cost and estimation methodology Project communication Risk Management Risk management and methods Risk identification Risk assessment methodologies Qualitative approach Semi-quantitative approach Quantitative approach Risk evaluation Risk treatment Risk in data centre migrations Migration Strategies Different data centre migration strategies Heterogeneous migration Homogeneous migration Physical migration Different IT transformations Pre-migration transformation Migration transformation Post-migration transformation Legal Aspects Regulatory requirements Contractual considerations Legal aspects when decommissioning High Level Discovery & Planning The importance of Business Service Reviews The concept of Availability The concept of Recoverability The importance of Service Level Objectives Requirements on designing the target IT architecture Information needed for high level planning Design Target Data Centre Requirements for the target data centre Sizing the data centre Architectural requirements Cooling requirements Power requirements Security Detailed Discovery and Planning The importance of discovery Automated discovery tools Asset management Network and system dependencies Detailed migration planning Migration waves Staffing Warranties and insurance Safety Safety precautions Technical safety review Electrical safety Lifting Personal safety during migration Fire safety during migration Security Controversy between access and security Access control Managing security during migration Security during migration Key management Practical hints and tips Continuous improvement Implementation Rehearsal Route investigation Resourcing Logistics team Packing Transport Installing the equipment Post migration support End of Project Why project closure Lessons learned Phased completion of project Criteria for project closure The outcome of the project End of project Exam: Certified Data Centre Migration Specialist Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Project Stakeholder Relationship Skills: In-House Training This course is designed to provide project managers with the ability to: Analyze the complexities of major stakeholder relationship categories Apply the most appropriate interpersonal relationship skills to the different categories of relationships Align the dynamic needs of the stakeholders with a project's objective throughout the project life cycle What you Will Learn Examine traditional and non-traditional ways to identify and assess stakeholders Explain how competence, character, and trust lead to project success and strong relationships with stakeholders Utilize 'Embodied Leadership' skills to build stakeholder relationships Apply stakeholder engagement best practices to case study and real-life scenarios Getting Started Stakeholders and project success Stakeholder management research Managing stakeholder relationships Identifying Stakeholders Stakeholder categories Stakeholder relationships across the project life cycle Tools and techniques for identifying stakeholders Assessing Stakeholders Assessing stakeholder relationships Recognizing stakeholder attitudes toward the project Analyzing stakeholders Using other types of stakeholder assessments Building Stakeholder Relationships The importance of psychological safety Building trust and getting results The anatomy of trust Navigating Challenging Situations Dynamics of conflict Responding to conflict Managing difficult conversations

Immerse yourself in the ancient practice of yoga, a transformative journey that extends far beyond the mere physical postures. Our comprehensive yoga sessions are meticulously curated and led by seasoned professionals, offering a serene sanctuary tailored for the demands of modern-day professionals yearning for holistic balance and wellness. Delve into innate human abilities such as intuition, telepathy, clairvoyance, lucid dreaming, and energy healing. Uncover these dormant gifts existing within and enjoy awakening them fully.

Duration 1 Days 6 CPD hours This course is intended for The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don?t need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful. This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. Prerequisites Prerequisite certification is not required before taking this course. Successful Azure AI Fundamental students start with some basic awareness of computing and internet concepts, and an interest in using Azure AI services. Specifically: Experience using computers and the internet. Interest in use cases for AI applications and machine learning models. A willingness to learn through hands-on exp... 1 - Fundamental AI Concepts Understand machine learning Understand computer vision Understand natural language processing Understand document intelligence and knowledge mining Understand generative AI Challenges and risks with AI Understand Responsible AI 2 - Fundamentals of machine learning What is machine learning? Types of machine learning Regression Binary classification Multiclass classification Clustering Deep learning Azure Machine Learning 3 - Fundamentals of Azure AI services AI services on the Azure platform Create Azure AI service resources Use Azure AI services Understand authentication for Azure AI services 4 - Fundamentals of Computer Vision Images and image processing Machine learning for computer vision Azure AI Vision 5 - Fundamentals of Facial Recognition Understand Face analysis Get started with Face analysis on Azure 6 - Fundamentals of optical character recognition Get started with Vision Studio on Azure 7 - Fundamentals of Text Analysis with the Language Service Understand Text Analytics Get started with text analysis 8 - Fundamentals of question answering with the Language Service Understand question answering Get started with the Language service and Azure Bot Service 9 - Fundamentals of conversational language understanding Describe conversational language understanding Get started with conversational language understanding in Azure 10 - Fundamentals of Azure AI Speech Understand speech recognition and synthesis Get started with speech on Azure 11 - Fundamentals of Azure AI Document Intelligence Explore capabilities of document intelligence Get started with receipt analysis on Azure 12 - Fundamentals of Knowledge Mining with Azure Cognitive Search What is Azure Cognitive Search? Identify elements of a search solution Use a skillset to define an enrichment pipeline Understand indexes Use an indexer to build an index Persist enriched data in a knowledge store Create an index in the Azure portal Query data in an Azure Cognitive Search index 13 - Fundamentals of Generative AI What is generative AI? Large language models What is Azure OpenAI? What are copilots? Improve generative AI responses with prompt engineering 14 - Fundamentals of Azure OpenAI Service What is generative AI Describe Azure OpenAI How to use Azure OpenAI Understand OpenAI's natural language capabilities Understand OpenAI code generation capabilities Understand OpenAI's image generation capabilities Describe Azure OpenAI's access and responsible AI policies 15 - Fundamentals of Responsible Generative AI Plan a responsible generative AI solution Identify potential harms Measure potential harms Mitigate potential harms Operate a responsible generative AI solution Additional course details: Nexus Humans AI-900T00 - Microsoft Azure AI Fundamentals training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the AI-900T00 - Microsoft Azure AI Fundamentals course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for The Power BI in a Day course is designed for beginners and intermediate users of Power BI. Overview #NAME? Students will discover the full capabilities of Power BI in a one-day, hands-on workshop. Please Note: This workshop is primarily self-directed and students will work at their own pace while having access to an instructor for questions. 1 - Accessing & Preparing data Data Set Power BI Desktop Power BI Desktop ? Accessing Data Power BI Desktop ? Data Preparation 2 - Data Modeling and Exploration Power BI Desktop ? Data Modeling and Exploration Power BI Desktop ? Data Exploration Continued References 3 - Data Visualization Power BI Desktop Power BI Desktop ? Data Visualization References 4 - Publishing & Accessing Reports Power BI Desktop ? Creating Mobile View Power BI Service Power BI Service ? Publishing Report Power BI Mobile ? Accessing Report on Mobile Device Power BI Service ? Collaboration and Distribution References 5 - Dashboard and Collaboration Power BI Service Building Dashboard References Additional course details: Nexus Humans Power BI: Dashboard in a Day training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Power BI: Dashboard in a Day course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for The primary audience for this course is data professionals who are familiar with data modeling, extraction, and analytics. It is designed for professionals who are interested in gaining knowledge about Lakehouse architecture, the Microsoft Fabric platform, and how to enable end-to-end analytics using these technologies. Job role: Data Analyst, Data Engineer, Data Scientist Overview Describe end-to-end analytics in Microsoft Fabric Describe core features and capabilities of lakehouses in Microsoft Fabric Create a lakehouse Ingest data into files and tables in a lakehouse Query lakehouse tables with SQL Configure Spark in a Microsoft Fabric workspace Identify suitable scenarios for Spark notebooks and Spark jobs Use Spark dataframes to analyze and transform data Use Spark SQL to query data in tables and views Visualize data in a Spark notebook Understand Delta Lake and delta tables in Microsoft Fabric Create and manage delta tables using Spark Use Spark to query and transform data in delta tables Use delta tables with Spark structured streaming Describe Dataflow (Gen2) capabilities in Microsoft Fabric Create Dataflow (Gen2) solutions to ingest and transform data Include a Dataflow (Gen2) in a pipeline This course is designed to build your foundational skills in data engineering on Microsoft Fabric, focusing on the Lakehouse concept. This course will explore the powerful capabilities of Apache Spark for distributed data processing and the essential techniques for efficient data management, versioning, and reliability by working with Delta Lake tables. This course will also explore data ingestion and orchestration using Dataflows Gen2 and Data Factory pipelines. This course includes a combination of lectures and hands-on exercises that will prepare you to work with lakehouses in Microsoft Fabric. Introduction to end-to-end analytics using Microsoft Fabric Explore end-to-end analytics with Microsoft Fabric Data teams and Microsoft Fabric Enable and use Microsoft Fabric Knowledge Check Get started with lakehouses in Microsoft Fabric Explore the Microsoft Fabric Lakehouse Work with Microsoft Fabric Lakehouses Exercise - Create and ingest data with a Microsoft Fabric Lakehouse Use Apache Spark in Microsoft Fabric Prepare to use Apache Spark Run Spark code Work with data in a Spark dataframe Work with data using Spark SQL Visualize data in a Spark notebook Exercise - Analyze data with Apache Spark Work with Delta Lake Tables in Microsoft Fabric Understand Delta Lake Create delta tables Work with delta tables in Spark Use delta tables with streaming data Exercise - Use delta tables in Apache Spark Ingest Data with DataFlows Gen2 in Microsoft Fabric Understand Dataflows (Gen2) in Microsoft Fabric Explore Dataflows (Gen2) in Microsoft Fabric Integrate Dataflows (Gen2) and Pipelines in Microsoft Fabric Exercise - Create and use a Dataflow (Gen2) in Microsoft Fabric

Catering to the demands of busy professionals, our virtual training programs are as effective as face-to-face learning. For more queries, reach out to us: info@mangates.com